Update app.py

app.py

@@ -18,7 +18,7 @@ st.markdown(
     unsafe_allow_html=True
 )
 st.markdown(
-    "<h4 style='text-align:center; color:#375a7f;'>AI-powered OSINT: See what the news & social web really think</h4>",
+    "<h4 style='text-align:center; color:#375a7f;'>AI-powered Open Source Research: See what the news & social web really think</h4>",
     unsafe_allow_html=True
 )
 st.markdown("---")
@@ -39,7 +39,7 @@ with col2:
         help="e.g. 'Military', 'worldnews', or leave blank for all"
     )
 with col3:
-    query = st.text_input("Enter your topic or query:", value="US Army INDOPACOM")
+    query = st.text_input("Enter your topic or query:", value="Response Here")
 
 max_articles = st.slider("Number of news articles:", 5, 25, 12)
 
@@ -62,7 +62,7 @@ def get_summary_with_hf_llm(keywords, sentiment_counts, top_subreddits, top_post
         f"Sentiment counts: {dict(sentiment_counts)}\n"
         f"Most active subreddits: {', '.join(top_subreddits)}\n"
         f"Example Reddit post titles: {', '.join(top_posts)}\n"
-        "Then, suggest 3-5 additional related search terms that could improve situational awareness."
+        "Then, suggest 3-5 additional related search terms that could improve situational awareness. In another paragraph suggest how the search results could be perceived by the US populace"
     )
 
     url = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
@@ -70,7 +70,6 @@ def get_summary_with_hf_llm(keywords, sentiment_counts, top_subreddits, top_post
         resp = requests.post(url, json={"inputs": prompt}, timeout=60)
         resp.raise_for_status()
         output = resp.json()
-        # Output format can vary; support both possible types:
         if isinstance(output, list) and "generated_text" in output[0]:
             return output[0]["generated_text"]
         elif isinstance(output, dict) and "generated_text" in output:
@@ -121,7 +120,7 @@ if st.button("🚀 Analyze!"):
 
         st.success(f"Found {len(reddit_data)} Reddit posts. Sentiment analysis complete.")
 
-        # --- Sentiment Results Table & Plot ---
+        # --- Sentiment Results Table & Pie/Donut Chart ---
         results_df = pd.DataFrame(reddit_data)
         results_df['sentiment'] = sentiment_results
 
@@ -132,51 +131,4 @@ if st.button("🚀 Analyze!"):
         sentiment_counts = sentiment_counts[sentiment_counts.index.str.strip() != '']
 
         sentiment_df = pd.DataFrame({
-            'Sentiment': sentiment_counts.index,
-            'Count': sentiment_counts.values
-        })
-
-        # Nice color palette for bars
-        palette = ['#183153', '#3277b3', '#375a7f', '#3bb273', '#ffb347', '#e05a47']
-
-        if not sentiment_df.empty and sentiment_df['Sentiment'].nunique() > 0:
-            fig = px.bar(
-                sentiment_df,
-                x='Sentiment',
-                y='Count',
-                color='Sentiment',
-                color_discrete_sequence=palette,
-                text='Count',
-                labels={'Sentiment': 'Sentiment', 'Count': 'Count'},
-                title='Sentiment Distribution'
-            )
-            fig.update_traces(marker_line_width=1, textposition="outside")
-            fig.update_layout(
-                yaxis=dict(title='Count'),
-                xaxis=dict(title='Sentiment'),
-                showlegend=False,
-                plot_bgcolor="#f8fafc",
-                paper_bgcolor="#f8fafc",
-                font=dict(size=15),
-                margin=dict(t=60, b=60, r=40, l=40)
-            )
-            st.plotly_chart(fig, use_container_width=True)
-        else:
-            st.info("No valid sentiment data for plotting.", icon="😶")
-
-        # --- AI SUMMARY SECTION ---
-        # Get top 3 subreddits and top 3 post titles for summary
-        top_subreddits = results_df['subreddit'].value_counts().index[:3].tolist() if 'subreddit' in results_df else []
-        top_posts = results_df['body'].dropna().astype(str).str[:50].tolist()[:3] if 'body' in results_df else []
-
-        st.markdown("### 📝 AI-Generated Summary & Suggestions")
-        summary = get_summary_with_hf_llm(
-            keywords=keywords,
-            sentiment_counts=sentiment_counts,
-            top_subreddits=top_subreddits,
-            top_posts=top_posts,
-            user_query=query
-        )
-        st.info(summary)
-    else:
-        st.warning("No news articles found for that query. Try a different topic or broaden the date range.", icon="📰")
+            'Sentiment': sentiment